Machine-learned partial ticket value prediction

ABSTRACT

A ticket exchange server is configured to distribute tickets to an event based on predicted attendance. The ticket exchange server accesses a set of training data describing statistics of and attendance during a historical event for a plurality of historical events. Using the training data, the ticket exchange server trains a machine-learned model, which is configured to predict a likelihood of a seat at a stadium being vacant during an event based on real-time statistics. During an event at the stadium, the ticket exchange server detects a vacant seat associated with a first ticket of a first user. The ticket exchange server determines a value of a second ticket for the vacant seat at least in part by applying the machine-learned model to real-time statistics of the event and distributes the second ticket to a second user.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. application Ser.No. 17/109,102, filed Dec. 1, 2020, now U.S. Pat. No. ______, which isincorporated by reference in its entirety.

BACKGROUND

When an attendee purchases a ticket for a sporting event, the attendeetypically purchases a ticket for themselves to use at the sportingevent. The ticket may give the attendee access to general areas of thestadium, including food vendors and restrooms, and to a designated seatwithin a section of the stadium. During the sporting event, the attendeemay move between the general areas and their designated seat as desired.However, even if seats closer to action within the stadium (e.g., afield or court) become available once other attendees leave the sportingevent, the attendee may not be allowed to take the empty seats and maybe constrained to their original designated seat. Further, the emptyseats may still be considered “reserved” for the original attendees touse, even if they have permanently left the sporting event. Thus, thisvaluable space within the stadium may be left empty, rather than beingused to allow more people to attend the sporting event.

In some instances, an entity, such as the attendee or another personnear the sporting event, may distribute a new ticket for a vacated seat.Since the sporting event has already started, the new ticket may bevalued less than it was originally purchased for. However, the entitymay have difficulty determining a fair value for the new ticket. Forinstance, the new ticket may have more value if the sporting event has aclose score versus if the sporting event is a “wipeout” (e.g., one teamis winning easily). Accordingly, a system for determining the value ofthe ticket for the seat is needed.

Furthermore, the number of tickets distributed for a sporting event mayreflect a capacity of a stadium the sporting event is held in. Forexample, if a baseball game is being held in a stadium that seats 40,000people, 40,000 tickets or less may be distributed for the baseball game.However, even if all 40,000 tickets are distributed, the likelihood of40,000 people attending the game with those tickets is low. More likely,less than 40,000 people will actually attend the baseball game sincesome ticket holders may change their plans last minute. Additionally, ifthe baseball game is a likely to be an exciting game between two rivals,then more ticket holders are likely to attend the baseball game than abaseball game that is projected to be slower paced. Hence, if not enoughtickets are distributed for the stadium to be at capacity during thesporting event, potential attendees may have missed out on theopportunity to view the sporting event in-person due to lack ofavailable tickets.

SUMMARY

The following disclosure describes a ticket exchange server thatdistributes tickets for seats at an event. In particular, the ticketexchange server controls a set of locked seats in the stadium based onpresence of users within the stadium. The ticket exchange serverreceives requests for tickets to events within the stadium anddistributes tickets to client devices based on requests. The ticketexchange server determines users' presences via the users' clientdevices. When a user comes within a vicinity of their seat at the event,the ticket exchange server unlocks the seat for the user. The user maysit in their seat to watch the event or move about the stadium,returning to the seat intermittently. If the user decides to leave theevent early, they may surrender their seat (i.e., by indicating to theticket exchange server that they are leaving the event and will notreturn), and the ticket exchange server may provide a ticket for theseat to a new user for the remainder of the event and unlock the seat ofthe new user after detecting a presence of the new user at the seat.

The ticket exchange server also determines a number of tickets to makeavailable for an event at a stadium. In particular, the ticket exchangeserver uses a machine-learned model configured to predict attendance atthe event. The machine-learned model may be trained on historicalattendance data for historical events at the stadium, historicalopponents of a sports team playing at the event, and/or a historicalwin/loss record for the sports team. The ticket exchange server uses themachine-learned model to predict an attendance for the event and usesthe predicted attendance to identify and distribute a number of ticketsmore than the predicted attendance in an effort to maximize a number oftickets distributed for the event.

Furthermore, the ticket exchange server may distribute tickets forvacated seats at an event. In particular, the ticket exchange serveruses a machine-learned model to predict an expected likelihood that aseat within a stadium will be vacant over the course of a futuresporting event based on real-time statistics of the future sportingevent. The ticket exchange server may train the machine-learned model topredict expected likelihoods based on historical attendance athistorical sporting events in the stadium and historical sporting eventstatistics. The ticket exchange server detects a vacant seat at thestadium during a sporting event and applies the machine-learned model toreal-time statistics of the sporting event to determine a value of a newticket for the vacant seat. The ticket exchange server distributes thenew ticket for the vacant seat for a remaining portion of the sportingevent to an attendee of the sporting event.

The features and advantages described in the specification are not allinclusive and, in particular, many additional features and advantageswill be apparent to one of ordinary skill in the art in view of thedrawings, specification, and claims. Moreover, it should be noted thatthe language used in the specification has been principally selected forreadability and instructional purposes, and may not have been selectedto delineate or circumscribe the inventive subject matter.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a system environment for a ticket exchange server,according to one embodiment.

FIG. 2 is a high-level block diagram illustrating a detailed view of aticket exchange server, according to one embodiment.

FIG. 3 is a flowchart illustrating a process for distributing a ticketfor a vacated seat at an event, according to one embodiment.

FIG. 4 is a flowchart illustrating a process for distributing tickets toan event based on a predicted attendance, according to one embodiment.

FIG. 5 is a flowchart illustrating a process for distributing a ticketfor a vacant seat, according to one embodiment.

FIG. 6A illustrates a first user at a baseball game, according to oneembodiment.

FIG. 6B illustrates a first user at an unlocked seat, according to oneembodiment.

FIG. 6C illustrates a relocked seat vacated by the first user at thebaseball game, according to one embodiment.

FIG. 6D illustrates a second user at the baseball game, according to oneembodiment.

FIG. 6E illustrates a second user at the unlocked seat, according to oneembodiment.

FIG. 7 is a high-level block diagram illustrating physical components ofa computer, according to one embodiment.

The figures depict embodiments of the present invention for purposes ofillustration only. One skilled in the art will readily recognize fromthe following description that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles of the invention described herein.

DETAILED DESCRIPTION

FIG. 1 illustrates a system environment 100 for a ticket exchange server140, according to one embodiment. The ticker exchange system 140 isconnected to a number of client devices 120 used by attendees at anevent via a network 110. These various components are now described inadditional detail.

The client devices 120 are computing devices such as smart phones,laptop computers, desktop computers, or any other device that cancommunicate with the ticket exchange server 140 via the network 110. Theclient devices 120 may provide a number of applications, which mayrequire user authentication before a user can use the applications, andthe client devices 120 may interact with the ticket exchange server 140via an application. Though three client devices 120 are shown in FIG. 1, any number of client devices 120 may be connected to the ticketexchange server 140 in other embodiments. The client devices 120 may belocated within a region designated for an event, like a baseball stadium130. Other designated regions may include theaters, concert halls,courts, fields, and/or any other type of venue for events. For the sakeof simplicity, description herein will be limited to stadiums 130,though in practice, the methods described herein apply equally to anyother region or venue.

Furthermore, though the event at the stadium 130 may herein be referredto as a “sporting event” or “baseball game,” the event may be related toother visual or auditory shows, conferences, or meetings. Examples ofsuch events include football games, basketball games, tennis matches,golf tournaments, and/or another sporting event. Other examples of suchevents include movies, concerts, musicals, plays, academic conferences,talk shows, and the like.

The network 110 connects the client devices 120 to the ticket exchangeserver 140, which is further described in relation to FIG. 2 . Thenetwork 110 may be any suitable communications network for datatransmission. In an embodiment such as that illustrated in FIG. 1 , thenetwork 110 uses standard communications technologies and/or protocolsand can include the Internet. In another embodiment, the network 110 usecustom and/or dedicated data communications technologies.

FIG. 2 is a high-level block diagram illustrating a detailed view of aticket exchange server 140, according to one embodiment. The ticketexchange server 140 operates in real-time to offer tickets to sportingevents, to monitor a use of each seat in a stadium 130 during a sportingevent, and to predict attendance of future sporting events. The ticketexchange server 140 includes a user interface module 200, a ticketmodule 210, a training module 220, a schedule store 230, an event store240, an attendance model 250, a ticket store 260, an availability store270, a vacancy model 280, and a training store 290. In some embodiments,the ticket exchange server 140 may include more modules or models thanshown in FIG. 2 or one or more of the modules and models shown in FIG. 2may be combined within the ticket exchange server.

The user interface module 200 generates ordering interfaces for displayon client devices 120. The user interface module 200 receives, from aclient device 120, a request to view tickets for a sporting event, whichmay be scheduled to occur at a later time or date, which may becurrently occurring. The user interface module 200 requests tickets forthe sporting event from the ticket module 210. A ticket is an electronicor physical item that is used to grant a person access to the sportingevent and may be associated with a designated seat or section within thestadium 130.

The user interface module 200 generates an ordering interface offeringtickets for the sporting event and transmits the ordering interface fordisplay via the client device 120. In some embodiments, instead ofoffering specific seats within the stadium, the ordering interfaceoffers tickets within a section, and users who select tickets via theordering interface are assigned seats in a selected section, eitherbefore or during the sporting event. In other embodiments, the orderinginterface offers tickets for specific seats within the stadium 130. Theordering interface may include interactive elements which the user mayinteract with to receive information about the event or the stadium 130,or to purchase one or more tickets. The user interface module 200facilitates purchases of tickets along with refunding deposits, offeringincentives, and distributing rebates. In particular, the user interfacemodule 200 may receive indications from the ticket module 210 if seatsat an ongoing sporting event are increasing the value, and if so, theuser interface module 200 may offer rebates to users who voluntarilysurrender their seats and leave the ongoing sporting event so the userinterface module 200 may offer the seats for sale at a higher value thanthe seats were initially sold for.

In some embodiments, the user interface module 200 may narrow theselection of tickets presented via the ordering interface based on useraccount information associated with the client device 120. For example,users may need a VIP account to access a VIP section of seats at theevents, so if the user of the client device 120 does not have the VIPaccount, the user interface module 200 may not offer tickets for the VIPsection in the ordering interface.

The user interface module 200 may also retrieve schedule information forthe event from schedule store 230. The schedule information for theevent includes a date of the event, an estimated time range of theevent, and a location of the event (i.e., a stadium), and the userinterface module 200 may display the schedule information to theordering interface. The user interface module 200 may display theschedule information to the ordering interface for users to view as theybrowse tickets for the sporting event.

The user interface module 200 receives requests for tickets from clientdevices 120 via the ordering interface and facilitates purchases of thetickets via the ordering interface. For each purchased ticket, the userinterface module 200 may indicate that the ticket was sold to the ticketmodule 210 and generate a ticket interface showing an electronic,scannable ticket for a seat at the sporting event. The ticket may be fora particular seat in the stadium or a section of the stadium, such thatwhen the user arrives to the section for the event, they are assigned aseat within the section. The ticket interface may also include aninteractive map of the stadium, event information retrieved from theevent store 240, and one or more interactive elements that a user mayinteract with to indicate usage of their seat at the event. For example,while at the event, the user may display the ticket interface to ascanner system or teller to gain entrance to the event. Furthermore, theuser may interact with the ticket interface to indicate that they are attheir seat or that they would like to surrender their seat when theyleave the sporting event before its conclusion.

The ticket module 210 identifies available tickets for sporting events.Upon receiving a request from the user interface module 200 for ticketsto a sporting event, the ticket module 210 accesses a schedule store230, which stores schedule information describing sporting events at oneor more stadiums 130. For a given stadium 130, the schedule informationmay include a time and date of the event and entities at the event(i.e., teams playing each other, a speaker giving a talk, a singerperforming, a movie playing, etc.). For the sporting event, the ticketmodule 210 may retrieve event information from the event store 240.Event information includes information describing a home team, anopponent team, a historical win/loss record of each team, and historicalgame statistics each team. The event store 240 may also store real-timestatistics about sporting events, such as a score, playing statisticsper player (e.g., batting or pitching statistics), team rank, playervalues, or any other game statistic describing the sporting event whilethe sporting event is occurring. These real-time statistics may bestored as event information for future sporting events once the sportingevent is over.

For a future sporting event (e.g., those that are scheduled to occur atthe stadium 130 at a future time), the ticket module 210 determinestickets for the future sporting event and sends the tickets to the userinterface module 200 to be distributed via the ordering interface. Inparticular, the ticket module 210 may determine a number of tickets todistribute for the future sporting event responsive to a triggeringevent that indicates that tickets for the future sporting event shouldbe distributed. For instance, the triggering event may be an indicationfrom an administrator, received from the user interface module 200,indicating a time and date that tickets for the future sporting eventshould become available. In another instance, the triggering event maybe reaching a threshold amount of time until the future sporting eventis scheduled to occur (e.g., a baseball game will occur in 1 month).Further, the triggering event may be a request received via the orderinginterface for a ticket to the future sporting event.

For the future sporting event, the ticket module 210 retrieves eventinformation for the future sporting event from the event store 240 andinputs the event information into the attendance model 250. In someembodiments, the attendance model also takes calendar data including oneor more of a day of the week of the future sporting event, a time of thesporting event, and a schedule of games for a sports team playing at thesporting event as input. The attendance model 250 is a machine-learnedmodel trained by the training module 220 to predict attendance at anevent. The attendance model 250 may be a neural network (such as a DNNor CNN), a classifier, a regression model, or any suitablemachine-learned model. The attendance model 250 returns a predictedattendance for the future sporting event to the ticket module 210. Thepredicted attendance is a predicted amount of people who attend thesporting event. For example, the attendance model 250 may predict ahigher attendance for a baseball game against a local rival team than abaseball game against a new baseball team from another geographicregion. In some cases, the attendance model may return a predictedattendance for each section of the stadium 130.

Based on the predicted attendance of the future sporting event, theticket module 210 determines a number of tickets to make available forthe future sporting event. The number of tickets to make available maybe greater than the capacity of the stadium 130 to optimize an amount oftickets sold based on the predicted attendance for the future sportingevent. For example, if the predicted attendance is 36,000 attendees,which is 90% of the capacity of the stadium 130, the ticket module 210may make 45,000 tickets available since the likely attendance is lessthan the capacity of the stadium. Increasing the number of availabletickets to more than capacity may increase the number of attendees whoactually attend the future sporting event. In another example, theticket module 210 may determine a higher number of tickets to distributefor a sporting event with a first predicted attendance (e.g., 30,000people) that is less than a second predicted attendance (e.g., 35,000people) in an effort to increase the actual attendance at the sportingevent by offering more tickets.

Furthermore, in some instances, the ticket module 210 may determinesections of the stadium 130 to increase available tickets for. Inparticular, each seat in the stadium 130 may be located in a particularsection the stadium 130. Some sections of the stadium 130, like asection located in the front and center of the stadium 130, may be moredesirable to attendees than other sections, like a section located inthe upper decks or outfield of the stadium. The ticket module 210 maydetermine, based on historical ticket sales data, which sections of thestadium 130 are the most popular among attendees and make tickets forsections available based on the popularity of the sections. For example,for two sections of the stadium 210 with the same number of seats, theticket module 210 may make more tickets available for the less popularof the two sections since attendees with tickets to the more popularsection are more likely to attend the future sporting event.

The ticket module 210 stores the tickets for the future sporting eventin ticket store 260. The ticket module 210 may store ticket informationalong with each ticket in the ticket store 260. Ticket informationincludes a section or seat in the stadium 130 associated with theticket. The ticket module 210 sends the tickets to the user interfacemodule 200 to offer for sale via the ordering interface. In someembodiments, the tickets may be sold for a standardized value based on asection of the stadium 130 or a type of sporting event (e.g., apre-season game, a championship game, etc.). Alternatively, the ticketmodule 210 may input the tickets, ticket information, and/or eventinformation to a machine-learned model configured to determine values oftickets. The ticket module 210 may store the determined values of thetickets as ticket information in the ticket store 260.

The ticket module 210 may send tickets to the user interface module 200upon request, or the user interface module 200 may directly access theticket store 260 to retrieve tickets and ticket information.Furthermore, as tickets are sold, the ticket module 210, upon receivingindications from the user interface module 200, updates the ticket store260 to reflect that the sold tickets are no longer available forpurchase and adds a name of a user associated with each purchased ticketto the ticket information. Alternatively, the user interface module 200may directly update the ticket store 260 to reflect the ticketavailability upon distributing one or more tickets via the orderinginterface.

As a sporting event is occurring, the ticket module 210 assigns seats tousers with tickets. To do so, the ticket module 210 monitors users'locations in real-time as they move about the stadium 130 to determinewhether a user has reached a designated section or seat indicated bytheir ticket. In particular, the ticket module 210 receives locationinformation from client devices 120 of users with tickets for thesporting event. The location information indicates a geographic locationof a user received from the user's client device 120.

In embodiments where tickets for the sporting event indicate adesignated section of the stadium 130, rather than a specific seat, theticket module 210 assigns each user a designated seat responsive todetermining the user is at the designated section in the stadium 130. Inparticular, when the ticket module 210 detects that a user is at thedesignated section indicated on their ticket, the ticket module accessesthe availability store 270 to determine which seats in the designatedsection are available. The availability store 270 stores seatinginformation including available (e.g., unassigned) and unavailable(e.g., assigned) seats within each section of the stadium 130. Theticket module 210 assigns the user the “best” seat available in thesection, i.e., a seat not already assigned to another user that isclosest to the front of the section (or another desirable portion of thesection). The ticket module 210 updates seating information in theavailability store 270 as seats are assigned to users such that theavailability store 270 contains seating information reflecting real-timeavailability within the stadium 130.

Furthermore, if the user is part of a group, the ticket module assignsall users in the group seats once one user for the group is at thesection. For a group of users, the ticket module 210 prioritizesassigning seats such that the group of users sits together during thesporting event. In particular, the ticket module 210 assigns seatslocated together within the section to the users in the group. Forexample, the ticket module 210 retrieves group data, which is storedwith the ticket information, from the ticket store 260. The group dataindicates which tickets are together in a group, which the ticket module210 may determine based on the tickets being purchased from one clientdevice 120 or through user account information linking the ticketstogether. The group data also indicates a plurality of client devices120 each associated with a ticket in the group. For a group of tickets,the ticket module 210 assigns users with the tickets seats in the samerow as one another or within a designated vicinity. The ticket module210 may also assign single users to seats near groups, rather thanrandom seats in the middle of the section, in order to maximizeavailable seats for other groups for tickets for the section. It shouldbe noted that when the ticket module 210 assigns a user the bestavailable seat within a section, the ticket module 210 can considergroups of tickets within the section that have not yet been assignedseats. Accordingly, if there is only one group of 4 consecutive seatsremaining in a section, and one group of 4 tickets that have not yetbeen assigned seats, if a different ticket holder arrives at thesection, the ticket module 210 will assign the ticket holder a seat thatis not within the group of 4 consecutive seats, even if the 4consecutive seats represent the best remaining seats within the section.Thus, the ticket module 210 can preserve groups of seats for sectionswith groups of ticket holders that have not yet arrived at the stadiumby not assigning the seats the ticket holders outside of the group.

In some embodiments, the ticket module 210 may automatically upgrade auser' when assigning a seat. For instance, if no seats are available inthe designated section of the user's ticket, the ticket module 210assigns the user a seat in a “better” section of the stadium 130.“Better” sections may have more expensive tickets, be closer to thefield, be located behind home plate, or otherwise be considered moredesirable than the designated section, for instance as indicated byhistorical ticket sales data. The same may be true for other events,like a basketball game, where the best seats are on the court, or amovie, where the best seats are in the back row. For a group of users,if the designated section of the user's tickets cannot accommodate oneor more of the users (due to seats in the designated section alreadybeing assigned to other users), the ticket module 210 upgrades all ofthe users in the group to seats in a better section. If no seats areavailable in a better section, the ticket module 210 may partiallyupgrade the group, provide a rebate to one or more users of the group,or take another remedial action.

The ticket module 210 may monitor the attendance at a sporting event inthe stadium 130 based on seating information in the availability storeand location data describing client devices of users with tickets at thestadium 130. The ticket module 210 stores real-time attendance data forthe event as event information in the event store 240. If the ticketmodule 210 determines, based on the monitored attendance at the sportingevent, that more users than available seats have arrived at the sportingevent, the ticket module 210 may send an indication to the userinterface module 200 to offer incentives to users at the sporting eventwho have not yet been assigned seats to release (i.e., give up) theirtickets. For instance, users who take an incentive may be provided witha full refund, a discount on future tickets, a cash rebate, and/or aticket for a future sporting event at the stadium 130. The ticket module210 may send a new indication to the user interface module 200 to stopoffering incentives once the attendance at the sporting event no longerindicates there are more users with unassigned seats than availableseats. The ticket module may also offer incentives for a specific userto surrender their seat responsive to detecting that their seat has avalue over a threshold if vacated and resold.

The ticket module 210 continues to monitor users' locations after theyhave reached their designated sections, and once a user has reachedtheir assigned seat in the stadium 130, the ticket module 210 unlockstheir assigned seat. For instance, each seat in the stadium 130 includesa locking mechanism that communicates with the ticket exchange server140. In some embodiments, the ticket exchange server 140 communicateswith the seats in the stadium 130 via the network 110, either directlyfrom the network 110 to the seats or through client devices 120 withinthe stadium 130. The ticket module 210 may communicate with the seats tounlock a seat in response to detecting a user's presence at the seat orin response to receiving an indication from the user's client device 120that the user is at the seat. The ticket module 210 may determine theuser's presence at the seat by receiving a GPS location from a clientdevice 120 of the user indicating that the user is within a thresholddistance of the seat. Likewise, the ticket module 210 may determine theuser's presence at the seat by receiving, via the seat, a near-fieldsignal from the client device 120 (such as an RFID signal). In someinstances, the ticket module may also charge a user a deposit for theirassigned seat upon unlocking their seat, which may be returned once theyleave the sporting event. For instance, if a user purchases a ticket forthe first 3 innings of a game, the user can pay a deposit which will berefunded if the user leaves the seat within a threshold amount of timeafter the end of the third inning.

The ticket module 210 also locks seats in the stadium 130. For instance,the ticket module 210 may lock all or a portion of the seats in thestadium 130 when the sporting event ends. The ticket module may alsolock a user's assigned seat in response to receiving an indication fromthe user, via their client device 120, that the user would like tosurrender their assigned seat. Surrendering a seat indicates to theticket module 210 that the user is leaving the sporting event and willnot return before conclusion of the sporting event. If a user surrenderstheir seat, the ticket module 210 may update the seating information inthe availability store 270 to indicate that the seat is no longerassigned to the user. In some instances, if the user responds to anoffer made by the user interface module 200 to surrender the seat, theuser may receive a rebate. Alternatively, the user may be charged adeposit when they are assigned the seat which is only returned if theysurrender the seat, which incentivizes the user to surrender the seatrather than only leave the sporting event. The user may need tosurrender their assigned seat within a threshold amount of time of beingassigned the seat to receive the deposit back or may be charged anadditional late fee if they do not surrender the seat within thethreshold amount of time. In some embodiments, a user's seat may beconsidered surrendered without explicit input from the user, forinstance if a location of the user is detected outside of the stadium(e.g., the user's client device 120 provides a location to the ticketexchange server 140 as the user walks away from the stadium), or if auser is located away from the seat for more than a threshold amount oftime.

During a sporting event, the ticket module 210 detects vacant seats inthe stadium 130. A vacant seat is a seat that is not assigned to a userfor the sporting event, such as one that was surrendered or neverassigned for the sporting event. The ticket module 210 receives seatinginformation for a plurality of seats in the stadium 130 in real-timeduring the sporting event from the availability store 270. For eachseat, seating information may include whether a seat is unlocked,locked, assigned to a user, surrendered by an assigned user, and/orunassigned. If the seat is or was assigned to a user, the seatinginformation includes information describing the ticket the user had forthe seat, such as the value of the ticket. The ticket module 210 maydetect a vacant seat by monitoring the seating information forsurrendered seats or by receiving an indication from a client device 120that a user is surrendering their seat at the sporting event.

If the ticket module 210 detects a vacant seat in the stadium 130, theticket module 210 accesses real-time statistics for the sporting eventfrom the event store 240. The ticket module 210 inputs one or more ofthe real-time statistics to the vacancy model 280 to determine a valuefor the seat. The vacancy model 280 is a machine-learned model trainedby the training module 220 to determine a likelihood that a seat will bevacant over the course of a sporting event. The vacancy model 280 may bea neural network, a classifier, a regression model, or any suitablemachine-learned model. In additional or alternative embodiments, theticket module 210 may input additional information to the vacancy model280. The additional information may be a section of the stadium 130 theseat is in or one or more previous values paid for the seat at thecurrent sporting event or previous sporting events, which may beobtained from the availability store 270 or the ticket store 260.

The vacancy model 280 returns an expected likelihood that the seat willbe empty during the sporting event, which the ticket module 210 uses todetermine a value of the seat. For instance, ticket module 210 mayassign a seat with a low expected likelihood of being empty a highervalue than a seat with a high expected likelihood of being empty. Theticket module 210 may use a tiered set of values based on the expectedlikelihood and a section of the seat to determine the value. The ticketmodule 210 sends the value to the user interface module 200 such thatthe user interface module 200 may offer a new ticket for the vacant seatat the value via the ordering interface. Alternatively, the userinterface module 200 may offer a ticket for the section of the seat,rather than the seat itself, such that a user who purchases the newticket is assigned the seat or another available seat in the sectionupon reaching the section. This distributing of the new ticket for avacated seat is referred to as “double-distributing.” The ticket module210 may detect a vacant seat any number of times, such that a ticket forthe same seat may be sold multiple times at the same sporting event,provided the seat has been surrendered multiple times. The ticket module210 may store the value for the ticket in the ticket store 260 as ticketinformation and/or in the availability store 270 as seating informationfor the seat.

In some embodiments, the ticket module 210 monitors real-time statisticsof an ongoing sporting event to determine if values of seats within thestadium are increasing based on the real-time statistics, which may actas a proxy for user demand. For example, if a baseball game started outwith a rival team highly likely to win, but in the 6th inning, the hometeam is winning, the ticket module 210 may determine that the value ofthe seats in the stadium 130 have increased. In other embodiments,tickets may be valued higher for close games than for blowouts, forgames consequential to playoff likelihoods than otherwise, for gameswith rival teams than games with non-rival teams, and the like. Theticket module 210 may analyze the real-time statistics by inputting thereal-time statistics to a machine-learned model configured to determinevalues of seats based on real-time statistics, a number of users leavingthe sporting event, and other event information about a sporting event.If the ticket module 210 determines that seats at the sporting eventcould be resold for values over a threshold amount, the ticket module210 may send an indication to the user interface module 200 to offerrebates to users at the sporting event who surrender their assignedseats. The ticket module may use a tiered system for determining valuesof the rebates based on the values of the seats or may use a standardvalue for each rebate.

The training module 220 trains one or more machine-learned modelsemployed by the ticket exchange system 140. In particular, the trainingmodule 220 trains the attendance model 250. The training module 220retrieves attendance training data stored in the training store 290. Theattendance training data includes historical attendance data forhistorical sporting events held at the stadium 130, historical opponentsof a home sports team during the sporting events at the stadium 130, andhistorical win/loss records of the home sports team. Historical sportingevents are those that have occurred in the past. The training module 220labels the historical sporting events with the attendance training dataand inputs the labeled sporting events into the attendance model 250 totrain the attendance model 250 to predict attendance at a futuresporting event. The training module 220 may store the labeled sportingevents in the training store 290 to use for future model training.

The training module 220 also trains the vacancy model 280. The trainingmodule 220 retrieves vacancy training data from the training store 290.The vacancy training data describes, for each of a plurality ofhistorical sporting events, attendance over the course of the historicalsporting event and statistics of the historical sporting event. Thevacancy training data my further include seating information in someinstances. The training module 220 labels, for each of the plurality ofhistorical sporting events, each seat at the stadium 130 of thehistorical sporting event based on the attendance training data andinputs the labeled seats into the attendance model 250 to train thevacancy model 280 to predict an expected likelihood that a seat is emptyor will become empty over the course of a sporting event. The trainingmodule 220 may store the labeled seats in the training store 290 to usefor future model training.

FIG. 3 is a flowchart illustrating a process 300 for double-distributinga ticket for a vacated seat at a sporting event, according to oneembodiment. Though reference is made to the ticket exchange server 140for this process 300, the process can be used by other online systems ormobile applications for double-distributing seats at other events.

The user interface module 200 provides 310 a first ticket to a firstclient device 120A of a first user in response to receiving a firstrequest from the seat from the first client device 120A. The ticketmodule 210 may provide the first ticket in response to charging thefirst user for the first ticket. In response to detecting a presence ofthe first user at the seat via the first client device 120A, the ticketmodule 210 unlocks 320 the seat for the first user. The user interfacemodule 200 receives 330 an indication from first client devicesurrendering the seat prior to a completion of the sporting event and,in response, the ticket module 210 locks the seat. In response toreceiving a second request for the seat from a second client device 120Bassociated with a second user, the user interface module 200 provides340 a second ticket for the seat for a remainder of the sporting eventto the second client device 120B. The ticket module 210 may provide thesecond ticket in response to charging the second user for the secondticket. The ticket module 210 unlocks 350 the seat in response todetecting, via the second client device 120B, a presence of the seconduser at the seat.

It is appreciated that although FIG. 3 illustrates a number ofinteractions according to one embodiment, the precise interactionsand/or order of interactions may vary in different embodiments. Forexample, in some embodiments, the first ticket may specify a firstsection of the stadium, and in response to detecting that the firstmobile device is located at the first section, the ticket module 210determines seat availability in the first section from seatinginformation stored in the availability store 270. The ticket module 210assigns the seat in the first section to the first user based on ticketavailability indicating that the seat is the best (e.g., closest to thefront of the first section) available seat in the first section. Infurther embodiments, the ticket module 210 may select the best availableseat subject to keeping a group of users, including the first user, withtickets for the first section together in the stadium 130.

The process 300 may also include the ticket module 210 upgrading userswhen a section of the stadium 130 is full. In particular, the ticketmodule 210 may assign, in response to the seat availability indicatingthat the first section is full, the seat to the first user in a secondsection of the stadium. In this instance, the second section has seatswith higher values than the first section. The process 300 may alsoinclude the ticket module 210 assigning tickets for a group of users.For instance, when the first ticket specifies a first section in thestadium 130, the ticket module 210 access group data that describes agroup of tickets in the first section including the first ticket and aplurality of client devices 120 of a plurality of users associated withthe tickets. In response to detecting that a client device 120 of theplurality of client devices 120 is located at the first section, theticket module 210 determines seat availability in the first section andassigns seats positioned together within the first section to each userof the plurality of users based on the seat availability.

FIG. 4 is a flowchart illustrating a process 400 for distributingtickets to a sporting event based on a predicted attendance, accordingto one embodiment. Though reference is made to the ticket exchangeserver 140 for this process 300, the process can be used by other onlinesystems or mobile applications for predict attendance at another event.

The training module 220 accesses 410 a set of training data for astadium 130 associated with a sports team. The training data describeshistorical attendance data for historical sporting events previouslyheld the stadium, historical opponents of the sporting team during thehistorical sporting events, and historical win/loss records of thesports team. The training module 220 trains 420 a machine-learned model,such as the attendance model 250, configured to predict attendance for afuture sporting event at the stadium based at least in part on a futureopponent of the sports team at the sporting event and a current orpredicted win/loss record for the sports team. The ticket module 210selects 430 a sporting event for the sports team against an opponent.The ticket module 210 may select a future sporting event that will occurin a threshold amount of time or based on receiving an indication forman administrator to determine a number of tickets for the sportingevent. The ticket module 210 determines 440 a predicted attendance forthe sporting event using the machine-learned model based at least inpart on the opponent and a current or predicted win/loss record for thesports team. The ticket module 210 identifies 450 a number of ticketsgreater than a capacity of the stadium to make available for the eventbased on the predicted attendance, and the user interface module 200distributes 460 up to the identified number of tickets to prospectiveattendees of the sporting event.

In other embodiments, the same process 400 may be used for other events.For example, other events may include concerts, movies, shows, andconferences. In these embodiments, the training data used to train themachine-learned model may describe historical attendance for otherevents held at the stadium, historical promotions of the other events,and historical attendance of similar events at other similar stadiums.Similar stadiums may be located within a threshold distance from thestadium 130, be located in a similar demographic area, have a comparablecapacity to the stadium, or host similar types of events to the stadium130. For example, a similar stadium to a baseball stadium 130 in NewYork City that holds 2,000 people would be a baseball stadium in Chicagothat holds 2,500 people.

It is appreciated that although FIG. 4 illustrates a number ofinteractions according to one embodiment, the precise interactionsand/or order of interactions may vary in different embodiments. Forexample, the user interface module 200 may send incentives to users torelease tickets for the sporting event responsive to the ticket module210 determining that more users than available seats have arrived at thesporting event. In another example, the ticket module 210 may determinethe predicted attendance to be greater if a sport team playing at thesporting event has a winning win/loss record rather than if the sportsteam has a losing win/loss record. Additionally, the ticket module 210may determine that the predicted attendance is greater if an opponent ofthe sports team is a divisional or local rival of the sports team thanif the opponent is not.

FIG. 5 is a flowchart illustrating a process 500 for distributing aticket for a vacant seat, according to one embodiment. Though referenceis made to the ticket exchange server 140 for this process 300, theprocess can be used by other online systems or mobile applications fordetecting vacant seats at another event.

The training module 220 accesses 510 a set of training data for astadium 130. The training data describes, for each of a plurality ofhistorical sporting events at the stadium, attendance over the course ofthe historical sporting event and statistics associated with thehistorical sporting event. The training module 220 trains 520 amachine-learned model, such as the vacancy model 280, on the trainingdata. The machine-learned model is configured to predict an expectedlikelihood of a seat within the stadium will be vacant over the courseof a future sporting event based on real-time statistics associated withthe future sporting event. The ticket module 210 detects 530 a vacantseat within the stadium during a sporting event, where the vacant seatis associated with a first ticket for the sporting event distributed toa first user. The user interface module 200 distributes 540 a secondticket for the vacant seat for a portion of the sporting event to asecond user. The ticket module 210 determines a value of the secondticket based at least in part by applying the machine-learned model toreal-time statistics of the sporting event. The ticket module 210 maydetermine that the value of the second ticket is greater if a differencebetween a score at the sporting event and a baseline score is below athreshold difference than if the difference is above-thresholddifference.

It is appreciated that although FIG. 5 illustrates a number ofinteractions according to one embodiment, the precise interactionsand/or order of interactions may vary in different embodiments. Forexample, the ticket module 210 may access a previous value paid by thesecond user for a previous seat at the sporting event prior to detectingthe vacant seat and send, to the user interface module 200, a value tocharge the second user based on the previous value and the determinedvalue. For instance, the ticket module may determine the value to chargeto be a difference between the previous value and the determined value.In another example, the ticket module 210 may determine the value of thesecond ticket for the vacant seat before the first user has vacated theseat by applying the machine-learned model. If the value exceeds athreshold, the user interface module 200 may send an incentive to aclient device 120 of the first user to vacate the seat and offer thesecond ticket for the value via the ordering interface in response toreceiving an indication from the first user's client device indicatingthat the first user has surrendered the seat.

FIGS. 6A-6E illustrates a first user 610A at a baseball game, accordingto one embodiment. In FIG. 6A, the first user 610A at a baseball game ismoving towards a set of locked seats 600 in a baseball stadium 130. Thefirst user 610A is carrying a client device 620A, which the ticketmodule 210 is communicating with to monitor the first user's 610Alocation in the baseball stadium 130. As shown on the scoreboard 630A,the baseball game has recently started. In FIG. 6B, the first user 610Ahas reached their seat 600B, so the ticket module 210 unlocked the seat600B responsive to detecting the first user's 610A presence at the seat600B. However, the ticket module 210 did not unlock seat 600A since theattendee's ticket was only for seat 600B.

In FIG. 6C, the first user 610A has left the baseball game before itsconclusion, as shown by the scoreboard 630B, and surrendered seat 600Bvia their client device 620A. In response, the ticket module 210 lockedthe seat 600B. The user interface module 200 distributed a new ticketfor the seat 600B to a second user 610B, who is moving towards the seat600B in FIG. 6D, before the baseball game has ended. The ticket exchangeserver 140 unlocks the seat 600B in response to detecting a presence ofthe second user 610B at the seat 600B via their client device 620B, asshown in FIG. 6E.

FIG. 7 is a high-level block diagram illustrating physical components ofa computer, according to one embodiment. Illustrated are at least oneprocessor 702 coupled to a chipset 704. Also coupled to the chipset 704are a memory 706, a storage device 708, a graphics adapter 712, and anetwork adapter 716. A display 718 is coupled to the graphics adapter712. In one embodiment, the functionality of the chipset 404 is providedby a memory controller hub 720 and an I/O controller hub 722. In anotherembodiment, the memory 706 is coupled directly to the processor 702instead of the chipset 704.

The storage device 708 is any non-transitory computer-readable storagemedium, such as a hard drive, compact disk read-only memory (CD-ROM),DVD, or a solid-state memory device. The memory 706 holds instructionsand data used by the processor 702. The graphics adapter 712 displaysimages and other information on the display 718. The network adapter 716couples the computer 700 to a local or wide area network.

As is known in the art, a computer 700 can have different and/or othercomponents than those shown in FIG. 7 . In addition, the computer 700can lack certain illustrated components. In one embodiment, a computer700 acting as a server may lack a graphics adapter 712, and/or display718, as well as a keyboard or pointing device. Moreover, the storagedevice 708 can be local and/or remote from the computer 700 (such asembodied within a storage area network (SAN)).

As is known in the art, the computer 700 is adapted to execute computerprogram modules for providing functionality described herein. As usedherein, the term “module” refers to computer program logic utilized toprovide the specified functionality. Thus, a module can be implementedin hardware, firmware, and/or software. In one embodiment, programmodules are stored on the storage device 708, loaded into the memory706, and executed by the processor 702.

Embodiments of the entities described herein can include other and/ordifferent modules than the ones described here. In addition, thefunctionality attributed to the modules can be performed by other ordifferent modules in other embodiments. Moreover, this descriptionoccasionally omits the term “module” for purposes of clarity andconvenience.

Other Considerations

The present invention has been described in particular detail withrespect to one possible embodiment. Those of skill in the art willappreciate that the invention may be practiced in other embodiments.First, the particular naming of the components and variables,capitalization of terms, the attributes, data structures, or any otherprogramming or structural aspect is not mandatory or significant, andthe mechanisms that implement the invention or its features may havedifferent names, formats, or protocols. Also, the particular division offunctionality between the various system components described herein ismerely for purposes of example, and is not mandatory; functionsperformed by a single system component may instead be performed bymultiple components, and functions performed by multiple components mayinstead performed by a single component.

Some portions of above description present the features of the presentinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. These operations, while describedfunctionally or logically, are understood to be implemented by computerprograms. Furthermore, it has also proven convenient at times, to referto these arrangements of operations as modules or by functional names,without loss of generality.

Unless specifically stated otherwise as apparent from the abovediscussion, it is appreciated that throughout the description,discussions utilizing terms such as “determining” or “displaying” or thelike, refer to the action and processes of a computer system, or similarelectronic computing device, that manipulates and transforms datarepresented as physical (electronic) quantities within the computersystem memories or registers or other such information storage,transmission or display devices.

Certain aspects of the present invention include process steps andinstructions described herein in the form of an algorithm. It should benoted that the process steps and instructions of the present inventioncould be embodied in software, firmware or hardware, and when embodiedin software, could be downloaded to reside on and be operated fromdifferent platforms used by real time network operating systems.

The present invention also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored on acomputer readable medium that can be accessed by the computer. Such acomputer program may be stored in a non-transitory computer readablestorage medium, such as, but is not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, magnetic-optical disks,read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, application specific integratedcircuits (ASICs), or any type of computer-readable storage mediumsuitable for storing electronic instructions, and each coupled to acomputer system bus. Furthermore, the computers referred to in thespecification may include a single processor or may be architecturesemploying multiple processor designs for increased computing capability.

The algorithms and operations presented herein are not inherentlyrelated to any particular computer or other apparatus. Variousgeneral-purpose systems may also be used with programs in accordancewith the teachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these systems will be apparent to those ofskill in the art, along with equivalent variations. In addition, thepresent invention is not described with reference to any particularprogramming language. It is appreciated that a variety of programminglanguages may be used to implement the teachings of the presentinvention as described herein, and any references to specific languagesare provided for invention of enablement and best mode of the presentinvention.

The present invention is well suited to a wide variety of computernetwork systems over numerous topologies. Within this field, theconfiguration and management of large networks comprise storage devicesand computers that are communicatively coupled to dissimilar computersand storage devices over a network, such as the Internet.

Finally, it should be noted that the language used in the specificationhas been principally selected for readability and instructionalpurposes, and may not have been selected to delineate or circumscribethe inventive subject matter. Accordingly, the disclosure of the presentinvention is intended to be illustrative, but not limiting, of the scopeof the invention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:accessing, for a stadium, a set of training data describing, for each ofa plurality of historical sporting events at the stadium, attendanceover the course of the historical sporting event and statisticsassociated with the historical sporting event; training, using the setof training data, a machine-learned model configured to predict anexpected likelihood that a seat within the stadium will be vacant overthe course of a future sporting event based on real-time statisticsassociated with the future sporting event; detecting, by a ticketexchange server in real-time, a seat within the stadium that wasinitially taken by a person during a sporting event but was then latervacated by the person during the sporting event, the vacated seatassociated with a first ticket for the sporting event distributed to afirst user and locked via a physical locking mechanism of the vacatedseat, wherein the ticket exchange server is configured to lock andunlock each seat in the stadium; providing, by the ticket exchangeserver, a rebate and refunding the deposit to the person in response todetecting that the person vacated the seat during the sporting event,wherein a value of the rebate is determined at least in part on anoutput of the machine-learned model; and distributing, via the ticketexchange server, a second ticket for the vacated seat for a portion ofthe sporting event to a mobile device of a second user.
 2. Thecomputer-implemented method of claim 1, wherein detecting the vacantseat within the stadium during a sporting event is in response toreceiving an indication from the first user surrendering the vacantseat.
 3. The computer-implemented method of claim 1, further comprising:in response to charging the second user for the second ticket,distributing the second ticket to the second user.
 4. Thecomputer-implemented method of claim 1, further comprising: accessing aprevious value paid by the second user for a previous seat at thesporting event prior to detecting the vacant seat; and charging thesecond user for the second ticket based on the previous value.
 5. Thecomputer-implemented method of claim 1, further comprising: determining,by applying the machine-learned model to real-time statistics of thesporting event, a value of the second ticket for the vacant seat beforethe first user has vacated the seat; in response to determining that thevalue of the second ticket exceeds a threshold, sending, to a clientdevice of the first user, an incentive to vacate the seat; and inresponse to receiving an indication from the first user surrendering thevacant seat prior to a completion of the sporting event, offering, viathe real-time ticket exchange server, the second ticket for the vacantseat for distribution.
 6. The computer-implemented method of claim 1,wherein a value of the second ticket is determined further based atleast in part on a section of the stadium the vacant seat is in.
 7. Thecomputer-implemented method of claim 1, wherein a value of the secondticket is greater if the sporting event has a below-threshold differencebetween the scores than if the sporting event has an above-thresholddifference between the scores.
 8. The computer-implemented method ofclaim 1, wherein the sporting event is one of a basketball game, abaseball game, a football game, a volleyball game, a soccer game, atennis match, a hockey game, and a rugby game.
 9. A non-transitorycomputer-readable storage medium comprising instructions that, whenexecuted by a hardware processor, cause the processor to perform stepscomprising: accessing, for a stadium, a set of training data describing,for each of a plurality of historical sporting events at the stadium,attendance over the course of the historical sporting event andstatistics associated with the historical sporting event; training,using the set of training data, a machine-learned model configured topredict an expected likelihood that a seat within the stadium will bevacant over the course of a future sporting event based on real-timestatistics associated with the future sporting event; detecting, by aticket exchange server in real-time, a seat within the stadium that wasinitially taken by a person during a sporting event but was then latervacated by the person during the sporting event, the vacated seatassociated with a first ticket for the sporting event distributed to afirst user and locked via a physical locking mechanism of the vacatedseat, wherein the ticket exchange server is configured to lock andunlock each seat in the stadium; providing, by the ticket exchangeserver, a rebate and refunding the deposit to the person in response todetecting that the person vacated the seat during the sporting event,wherein a value of the rebate is determined at least in part on anoutput of the machine-learned model; and distributing, via the ticketexchange server, a second ticket for the vacated seat for a portion ofthe sporting event to a mobile device of a second user.
 10. Thenon-transitory computer-readable storage medium of claim 9, whereindetecting the vacant seat within the stadium during a sporting event isin response to receiving an indication from the first user surrenderingthe vacant seat.
 11. The non-transitory computer-readable storage mediumof claim 9, the instructions further comprising instructions for: inresponse to charging the second user for the second ticket, distributingthe second ticket to the second user.
 12. The non-transitorycomputer-readable storage medium of claim 9, the instructions furthercomprising instructions for: accessing a previous value paid by thesecond user for a previous seat at the sporting event prior to detectingthe vacant seat; and instructions for charging the second user for thesecond ticket based on the previous value.
 13. The non-transitorycomputer-readable storage medium of claim 9, the instructions furthercomprising instructions for: determining, by applying themachine-learned model to real-time statistics of the sporting event, avalue of the second ticket for the vacant seat before the first user hasvacated the seat; in response to determining that the value of thesecond ticket exceeds a threshold, sending, to a client device of thefirst user, an incentive to vacate the seat; and in response toreceiving an indication from the first user surrendering the vacant seatprior to a completion of the sporting event, offering, via the real-timeticket exchange server, the second ticket for the vacant seat fordistribution.
 14. The non-transitory computer-readable storage medium ofclaim 9, wherein a value of the second ticket is determined furtherbased at least in part on a section of the stadium the vacant seat isin.
 15. The non-transitory computer-readable storage medium of claim 9,wherein a value of the second ticket is greater if the sporting eventhas a below-threshold difference between the scores than if the sportingevent has an above-threshold difference between the scores.
 16. Thenon-transitory computer-readable storage medium of claim 9, wherein thesporting event is one of a basketball game, a baseball game, a footballgame, a volleyball game, a soccer game, a tennis match, a hockey game,and a rugby game.
 17. A computer system comprising: a computerprocessor; and a non-transitory computer-readable storage medium storageinstructions that when executed by the computer processor performactions comprising: accessing, for a stadium, a set of training datadescribing, for each of a plurality of historical sporting events at thestadium, attendance over the course of the historical sporting event andstatistics associated with the historical sporting event; training,using the set of training data, a machine-learned model configured topredict an expected likelihood that a seat within the stadium will bevacant over the course of a future sporting event based on real-timestatistics associated with the future sporting event; detecting, by aticket exchange server in real-time, a seat within the stadium that wasinitially taken by a person during a sporting event but was then latervacated by the person during the sporting event, the vacated seatassociated with a first ticket for the sporting event distributed to afirst user and locked via a physical locking mechanism of the vacatedseat, wherein the ticket exchange server is configured to lock andunlock each seat in the stadium; providing, by the ticket exchangeserver, a rebate and refunding the deposit to the person in response todetecting that the person vacated the seat during the sporting event,wherein a value of the rebate is determined at least in part on anoutput of the machine-learned model; and distributing, via the ticketexchange server, a second ticket for the vacated seat for a portion ofthe sporting event to a mobile device of a second user
 18. The computersystem of claim 17, wherein detecting the vacant seat within the stadiumduring a sporting event is in response to receiving an indication fromthe first user surrendering the vacant seat.
 19. The computer system ofclaim 17, wherein a value of the second ticket is determined furtherbased at least in part on a section of the stadium the vacant seat isin.
 20. The computer system of claim 17, wherein a value of the secondticket is greater if the sporting event has a below-threshold differencebetween the scores than if the sporting event has an above-thresholddifference between the scores.